模型拟合期间 Keras 中的尺寸不匹配

问题描述

我在 Keras 中使用密集神经网络组合了一个 VAE.在 model.fit 期间，我发现尺寸不匹配，但不确定是什么导致代码失效.下面是我的代码的样子

I put together a VAE using Dense Neural Networks in Keras. During model.fit I get a dimension mismatch, but not sure what is throwing the code off. Below is what my code looks like

from keras.layers import Lambda, Input, Dense
from keras.models import Model
from keras.datasets import mnist
from keras.losses import mse, binary_crossentropy
from keras.utils import plot_model
from keras import backend as K
import keras

import numpy as np
import matplotlib.pyplot as plt
import argparse
import os

(x_train, y_train), (x_test, y_test) = mnist.load_data()

image_size = x_train.shape[1]
original_dim = image_size * image_size
x_train = np.reshape(x_train, [-1, original_dim])
x_test = np.reshape(x_test, [-1, original_dim])
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

# network parameters
input_shape = (original_dim, )
intermediate_dim = 512
batch_size = 128
latent_dim = 2
epochs = 50


x = Input(batch_shape=(batch_size, original_dim))
h = Dense(intermediate_dim, activation='relu')(x)
z_mean = Dense(latent_dim)(h)
z_log_sigma = Dense(latent_dim)(h)

def sampling(args):
    z_mean, z_log_sigma = args
    #epsilon = K.random_normal(shape=(batch, dim))
    epsilon = K.random_normal(shape=(batch_size, latent_dim))
    return z_mean + K.exp(z_log_sigma) * epsilon

# note that "output_shape" isn't necessary with the TensorFlow backend
# so you could write `Lambda(sampling)([z_mean, z_log_sigma])`
z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_sigma])

decoder_h = Dense(intermediate_dim, activation='relu')
decoder_mean = Dense(original_dim, activation='sigmoid')
h_decoded = decoder_h(z)
x_decoded_mean = decoder_mean(h_decoded)

print('X Decoded Mean shape: ', x_decoded_mean.shape)

# end-to-end autoencoder
vae = Model(x, x_decoded_mean)

# encoder, from inputs to latent space
encoder = Model(x, z_mean)

# generator, from latent space to reconstructed inputs
decoder_input = Input(shape=(latent_dim,))
_h_decoded = decoder_h(decoder_input)
_x_decoded_mean = decoder_mean(_h_decoded)
generator = Model(decoder_input, _x_decoded_mean)

def vae_loss(x, x_decoded_mean):
    xent_loss = keras.metrics.binary_crossentropy(x, x_decoded_mean)
    kl_loss = - 0.5 * K.mean(1 + z_log_sigma - K.square(z_mean) - K.exp(z_log_sigma), axis=-1)
    return xent_loss + kl_loss

vae.compile(optimizer='rmsprop', loss=vae_loss)


print('X train shape: ', x_train.shape)
print('X test shape: ', x_test.shape)

vae.fit(x_train, x_train,
        shuffle=True,
        epochs=epochs,
        batch_size=batch_size,
        validation_data=(x_test, x_test))

这是我在调用 model.fit 时看到的堆栈跟踪.

Here is the stack trace that I see when model.fit is called.

File "/home/asattar/workspace/projects/keras-examples/blogautoencoder/VariationalAutoEncoder.py", line 81, in <module>
    validation_data=(x_test, x_test))
  File "/usr/local/lib/python2.7/dist-packages/Keras-2.2.4-py2.7.egg/keras/engine/training.py", line 1047, in fit
    validation_steps=validation_steps)
  File "/usr/local/lib/python2.7/dist-packages/Keras-2.2.4-py2.7.egg/keras/engine/training_arrays.py", line 195, in fit_loop
    outs = fit_function(ins_batch)
  File "/usr/local/lib/python2.7/dist-packages/Keras-2.2.4-py2.7.egg/keras/backend/tensorflow_backend.py", line 2897, in __call__
    return self._call(inputs)
  File "/usr/local/lib/python2.7/dist-packages/Keras-2.2.4-py2.7.egg/keras/backend/tensorflow_backend.py", line 2855, in _call
    fetched = self._callable_fn(*array_vals)
  File "/home/asattar/.local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1439, in __call__
    run_metadata_ptr)
  File "/home/asattar/.local/lib/python2.7/site-packages/tensorflow/python/framework/errors_impl.py", line 528, in __exit__
    c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [128,784] vs. [96,784]
     [[{{node training/RMSprop/gradients/loss/dense_5_loss/logistic_loss/mul_grad/BroadcastGradientArgs}} = BroadcastGradientArgs[T=DT_INT32, _class=["loc:@train...ad/Reshape"], _device="/job:localhost/replica:0/task:0/device:CPU:0"](training/RMSprop/gradients/loss/dense_5_loss/logistic_loss/mul_grad/Shape, training/RMSprop/gradients/loss/dense_5_loss/logistic_loss/mul_grad/Shape_1)]]

请注意堆栈跟踪中的不兼容的形状:[128,784] vs. [96,784]"，位于跟踪的末尾.

Please note the "Incompatible shapes: [128,784] vs. [96,784]" in the stack trace" towards the end of the trace.

推荐答案

根据 Keras:如果数据的大小不能被batch_size整除怎么办?，最好使用model.fit_generator而不是model.fit 在这里.

According to Keras: What if the size of data is not divisible by batch_size?, one should better use model.fit_generator rather than model.fit here.

要使用model.fit_generator，应该定义自己的生成器对象.下面是一个例子:

To use model.fit_generator, one should define one's own generator object.Following is an example:

from keras.utils import Sequence
import math

class Generator(Sequence):
    # Class is a dataset wrapper for better training performance
    def __init__(self, x_set, y_set, batch_size=256):
        self.x, self.y = x_set, y_set
        self.batch_size = batch_size
        self.indices = np.arange(self.x.shape[0])

    def __len__(self):
        return math.floor(self.x.shape[0] / self.batch_size)

    def __getitem__(self, idx):
        inds = self.indices[idx * self.batch_size:(idx + 1) * self.batch_size]
        batch_x = self.x[inds]
        batch_y = self.y[inds]
        return batch_x, batch_y

    def on_epoch_end(self):
        np.random.shuffle(self.indices)

train_datagen = Generator(x_train, x_train, batch_size)
test_datagen = Generator(x_test, x_test, batch_size)

vae.fit_generator(train_datagen,
    steps_per_epoch=len(x_train)//batch_size,
    validation_data=test_datagen,
    validation_steps=len(x_test)//batch_size,
    epochs=epochs)

从 如何使用自定义生成器在每个 epoch 后洗牌?.

这篇关于模型拟合期间 Keras 中的尺寸不匹配的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！